With more than 4,000 shares, 20,000 comments, and 100,000 reactions on Facebook, the photo of the elderly woman, sitting behind her homemade 122nd birthday cake, has unquestionably gone viral. "I started decorating cakes from five years old," the caption reads, "and I can't wait to grow my baking journey." The picture is also unquestionably fake. If the curious candles – one seems to float in the air, attached to nothing – or the weird amorphous blobs on the cake in the foreground didn't give it away, then the fact the celebrant would be the oldest person in the world by almost five years should. Thankfully, the stakes for viral supercentenarian cake decorators are low.

We review several different languages for collective decision making problems, in which agents express their judgments, opinions, or beliefs over elements of a logically structured domain. Several such languages have been proposed in the literature to compactly represent the questions on which the agents are asked to give their views. In particular, the framework of judgment aggregation allows agents to vote directly on complex, logically related formulas, whereas the setting of binary aggregation asks agents to vote on propositional variables, over which dependencies are expressed by means of an integrity constraint. We compare these two languages and some of their variants according to their relative succinctness and according to the computational complexity of aggregating several individual views expressed in such languages into a collective judgment. Our main finding is that the formula-based language of judgment aggregation is more succinct than the constraint-based language of binary aggregation. In many (but not all) practically relevant situations, this increase in succinctness does not entail an increase in complexity of the corresponding problem of computing the outcome of an aggregation rule.

New research from Canada proposes a method to automatically rewrite an article as you read it, based on Tinder-style'swiping', or on passive observation of the reader's interaction with the various kinds of content that the article contains. The system, titled Hone As You Read (HARE), is presented in a paper from Western University at Ontario, Canada, with corresponding Python code at GitHub. The central idea of the project is that an article may contain various kinds of content, evolving (much like this one) from the headline down to further details. Later parts of an article may contain different kinds of supporting material, use cases, or hypotheses or conjecture about the ramifications of the news. Under HARE, if you don't like that kind of material, you can vote it away on a paragraph-by-paragraph basis while the system learns your preferences, so that by the time you scroll down, content similar to the material that you'downvoted' has already been removed or rewritten.

GPT-3 has created a lot of buzz since its release a few months ago. The system can generate (almost) plausible conversations with the likes of Nietzsche, write op eds for The Guardian and was even used successfully to post undercover comments on Reddit for a week. But even with GPT-3, AI is still stuck in Uncanny Valley. GPT-3 output feels like it was written by a human at first glance, but it isn't quite. On closer inspection, it lacks substance and coherence.

Seeking to call into question the mental acuity of his opponent, Donald Trump looked across the presidential debate stage at Joseph Biden and said, "So you said you went to Delaware State, but you forgot the name of your college. Biden chuckled, but viewers may have been left wondering: did the former vice president misstate where he went to school? Those who viewed the debate live on an app from the London-based company Logically were quickly served an answer: the president's assertion was false. A brief write-up posted on the company's website the next morning provided links to other fact-checks from National Public Radio and the Delaware News Journal on the same claim, which explain that Biden actually said his first Senate campaign received a boost from students at the school. Logically is one of a number of efforts, both commercial and academic, to apply techniques of artificial intelligence (AI), including machine learning and natural language processing (NLP), to identify false ...

One of the latest collaborations between artificial intelligence and humans is further evidence of how machines and humans can create better results when working together. Artificial intelligence (AI) is now on the job to combat the spread of misinformation on the internet and social platforms thanks to the efforts of start-ups such as Logically. While AI is able to analyze the enormous amounts of info generated daily on a scale that's impossible for humans, ultimately, humans need to be part of the process of fact-checking to ensure credibility. As Lyric Jain, founder and CEO of Logically, said, toxic news travels faster than the truth. Our world desperately needs a way to discern truth from fiction in our news and public, political and economic discussions, and artificial intelligence will help us do that.

The final part of my Life series is still in the works but I need to interrupt that series with some exciting news. I will likely do a whole series on Bean Machine later on this autumn, but for today let me just give you the brief overview should you not want to go through the paper. As the paper's title says, Bean Machine is a Probabilistic Programming Language (PPL). For a detailed introduction to PPLs you should read my "Fixing Random" series, where I show how we could greatly improve support for analysis of randomness in .NET by both adding types to the base class library and by adding language features to a language like C#. If you don't want to read that 40 post introduction, here's the TLDR.

Artificial word has created hype in the digital age, and everyone is curious to know what it is actually? And how does that work? The adjective is ambiguous because all the things created by humans considered artificial. AI is the science concept, as a branch of science call this way a bunch of algorithms which do learn from data-experience, and in this way, they are called artificial, but they learn naturally. Many of the models of learning has been taken from nature, the artificial neural networks (ANNs), whose model was first presented by Many Scientist, among them are Newton, Thomas Edison, in the early 40s.

We examine the class of languages that can be defined entirely in terms of provability in an extension of the sorted type theory (Ty_n) by embedding the logic of phonologies, without introduction of special types for syntactic entities. This class is proven to precisely coincide with the class of logically closed languages that may be thought of as functions from expressions to sets of logically equivalent Ty_n terms. For a specific sub-class of logically closed languages that are described by finite sets of rules or rule schemata, we find effective procedures for building a compact Ty_n representation, involving a finite number of axioms or axiom schemata. The proposed formalism is characterized by some useful features unavailable in a two-component architecture of a language model. A further specialization and extension of the formalism with a context type enable effective account of intensional and dynamic semantics.

Shapley's impossibility result indicates that the two-person bargaining problem has no non-trivial ordinal solution with the traditional game-theoretic bargaining model. Although the result is no longer true for bargaining problems with more than two agents, none of the well known bargaining solutions are ordinal. Searching for meaningful ordinal solutions, especially for the bilateral bargaining problem, has been a challenging issue in bargaining theory for more than three decades. This paper proposes a logic-based ordinal solution to the bilateral bargaining problem. We argue that if a bargaining problem is modeled in terms of the logical relation of players' physical negotiation items, a meaningful bargaining solution can be constructed based on the ordinal structure of bargainers' preferences. We represent bargainers' demands in propositional logic and bargainers' preferences over their demands in total preorder. We show that the solution satisfies most desirable logical properties, such as individual rationality (logical version), consistency, collective rationality as well as a few typical game-theoretic properties, such as weak Pareto optimality and contraction invariance. In addition, if all players' demand sets are logically closed, the solution satisfies a fixed-point condition, which says that the outcome of a negotiation is the result of mutual belief revision. Finally, we define various decision problems in relation to our bargaining model and study their computational complexity.